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Proceedings Paper

A simplified method based on terrain complexity for lidar point cloud and its uncertainty analysis
Author(s): Qianning Zhang; Zechun Huang; Haibin Shang; Andong Hong; Zhu Xu
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Paper Abstract

LiDAR is a technology to acquire object surface measurements which intergrates GPS, IMU, laser scanning and ranging system and imaging devices together. LIDAR technology has the characteristics of highly automation, short data production cycle, the little effect of external environment and high precision and accuracy to acquire measurement information. But the number of liDAR point cloud is huge. When using large amounts of point cloud data to construct DEM, instead of improving the accuracy of DEM no significant effect, it will lead to the rapid decline in data processing speed. So it is necessary to simplify the LiDAR point cloud. When simplifying the point cloud, the criterions of point cloud simplification directly influence the distribution and quality of retention points. Usually, the point simplification criterions are based on topographic feature. Hence,this paper will proposal a new approach based on terrain complexity metrics to simplify LiDAR point cloud. Terrain complexity index present a comprehensive description of topographic features. First the index is calculated based on the existing rough precision DEM data;next,find out the point cloud simplification threshold according to the index;then set simplify rules to retain the feature points and simplify the useless points;finally, using geostatistical method,high accuracy DEM is constructed by the retention points and the precision and accuracy of LiDAR point cloud simplification is evaluated. The method will be expected to improve the precision and accuracy of LiDAR point cloud simplification.

Paper Details

Date Published: 9 December 2015
PDF: 11 pages
Proc. SPIE 9808, International Conference on Intelligent Earth Observing and Applications 2015, 98080W (9 December 2015); doi: 10.1117/12.2207609
Show Author Affiliations
Qianning Zhang, Southwest Jiaotong Univ. (China)
Zechun Huang, Southwest Jiaotong Univ. (China)
Haibin Shang, Chengdu Univ. of Technology (China)
Andong Hong, Southwest Jiaotong Univ. (China)
Zhu Xu, Southwest Jiaotong Univ. (China)

Published in SPIE Proceedings Vol. 9808:
International Conference on Intelligent Earth Observing and Applications 2015
Guoqing Zhou; Chuanli Kang, Editor(s)

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